Check out our latest tutorial for conducting auditory classification within a Gradient Notebook, using various techniques with TensorFlow to classify bird calls!
In this tutorial, we examine the Few-Shot Learning paradigm for deep and machine learning tasks. Readers can expect to learn what it is, different techniques for implementation, and details about use cases for Few-Shot Learning.
In our newest blog overview, we look at AMPT-GA: a system that selects application-level data precisions to maximize performance while satisfying accuracy constraints via automatic mixed precision training
In this tutorial, we show how to implement unpaired image-to-image translation with Cycle GANs from scratch in TensorFlow using a clever combination of a ResNet generator and Patch GAN discriminator.
In this article, we ask what GPU memory bandwidth is, and examine why it should be taken into consideration as one of the qualities an ML/DL expert should look for in a machine learning platform.
In the continuation of our series on writing deep learning models from scratch with PyTorch, we show how to create, train, and evaluate a ResNet neural network for image classification with the CIFAR-100 dataset.
In this tutorial, we look in depth at Pix4Dmatic: the premiere software for photogrammetry at scale. Readers can expect to learn how to set up Pix4Dmatic on Core, walk through all the steps for staging a task, and then create a 3d model using provided sample data.
Follow this tutorial to learn what attention in deep learning is, and why attention is so important in image classification tasks. We then follow up with a demo on implementing attention from scratch with VGG.
Free notebooks can be run for 6 hours at a time.
More info available in docs: https://docs.paperspace.com/gradient/machines/#free-machines...